Lixin YANG | 杨理欣
Morning, I’m a fourth year PhD candidate in the department of
Computer Science, Shanghai Jiao Tong University (SJTU).
Starting from 2019, I have been in Machine Vision and Intelligence Group
under the
supervision of Prof. Cewu Lu. Prior to that, I received my
M.S degree at the Intelligent Robot Lab in SJTU.
My research interests include Computer Vision, Robotic
Vision,
3D Vision and Graphics.
Currently, I am focusing on modeling and imitating the interaction of hand manipulating objects,
including 3D hand pose and shape from X,
hand-object reconstruction, animation and synthesis.
I am also interested in NERF and motion
retargeting.
I am looking for cooperation and self-motivated interns. Contact me if you are interested in the
above topics
Email  / 
Google
Scholar  / 
GitHub  / 
LinkedIn  / 
Twitter  / 
Resumé (PDF)
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- [2023.02]
🎉 One paper: POEM is accepted by CVPR 2023.
- [2022.10]
👩🏻❤️👨🏻 I got married to my beautiful beloved girl.
- [2022.10]
📢 Invited Talk at IDEA, Thanks Ailing Zeng for hosting.
- [2022.09] 🎉
DART got accepted by NeurIPS
2022 - Datasets and Benchmarks Track.
- [2022.07]
📢 Invited Talk at 智东西公开课 | AI新青年讲座: 基于图像的手物交互重建与虚拟人手生成 视频
(中文).
- [2022.04]
📢 Invited Talk at MPI-IS Perceiving Systems. Thanks Yuliang Xiu for hosting
(INFO).
- [2022.03]
🎉 Two paper were accepted by CVPR 2022:
one Oral, one poster.
- [2021.07]
🎉 One paper got accepted by ICCV 2021.
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POEM: Reconstructing Hand in a Point Embedded Multi-view Stereo
Lixin Yang, 
Jian Xu, 
Licheng Zhong, 
Xinyu Zhan, 
Zhicheng Wang, 
Kejian Wu, 
Cewu Lu
CVPR, 2023
Coming Soon
Details
POEM (Point Embedded Multi-view) focuses on reconstructing an articulation body with "true
scale" and "accurate pose" from a series of sparsely arranged camera views. In practice, we
used the example of hand. POEM explores the power of points, using a cluster of (x, y, z)
coordinates with natural positional encoding to find associations in multi-view stereo.
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DART: Articulated Hand Model with Diverse Accessories and Rich Textures
Daiheng Gao*,  
Yuliang Xiu*, 
KaiLin Li*, 
Lixin Yang*,
Feng Wang, Peng Zhang, Bang Zhang,
Cewu Lu,
Ping Tan
NeurIPS, 2022 - Datasets and Benchmarks Track
Website
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Paper
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arXiv
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Code
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Video
Details
We extend MANO with more Diverse Accessories and Rich Textures, namely DART.
DART is comprised of 325 exquisite hand-crafted texture maps which vary in appearance and cover
different kinds of blemishes, make-ups, and accessories.
We also generate large-scale (800K), diverse, and high-fidelity hand images, paired with
perfect-aligned 3D labels, called DARTset.
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OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction
Lixin Yang*,
Kailin Li*
Xinyu Zhan*,
Fei Wu,
Anran Xu,
Liu Liu,
Cewu Lu
(*=equal contribution)
CVPR, 2022
Website
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Paper
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arXiv
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Toolkit
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Tink
Details
Learning how humans manipulate objects requires machines to acquire knowledge from two perspectives:
one for understanding object affordances and the other for learning human’s interactions based on
the affordances. In this work, we propose a multi-modal and rich-annotated knowledge repository,
OakInk, for visual and cognitive understanding of hand-object interactions.
Check our website for more details !
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ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration
and Synthesis
Lixin Yang*,
Kailin Li*
Xinyu Zhan,
Jun Lv,
Wenqiang Xu,
Jiefeng Li,
Cewu Lu
(*=equal contribution)
CVPR, 2022   (Oral Presentation)
Paper
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arXiv
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Code
Details
We propose ArtiBoost, a lightweight online data enhancement method that boosts articulated
hand-object pose
estimation from the data perspective
ArtiBoost can cover diverse hand-object poses and camera viewpoints through sampling in a
Composited hand-object Configuration and Viewpoint space (CCV-space) and can adaptively enrich the
current hard-discernable items by loss-feedback and sample re-weighting.
ArtiBoost alternatively performs data exploration and synthesis within a learning pipeline,
and those synthetic data are blended into real-world source data for training.
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CPF: Learning a Contact Potential Field to Model the Hand-Object Interaction
Lixin Yang,
Xinyu Zhan,
Kailin Li,
Wenqiang Xu,
Jiefeng Li,
Cewu Lu
ICCV, 2021
Project
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Paper
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Supp
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arXiv
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Code
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知乎
Details
Modeling the hand-object (HO) interaction not only requires estimation of the HO pose,
but also pays attention to the contact due to their interaction. In this paper,
we present an explicit contact representation namely Contact Potential Field (CPF),
and a learning-fitting hybrid framework namely MIHO to Modeling the Interaction of Hand and Object.
In CPF, we treat each contacting HO vertex pair as a spring-mass system.
Hence the whole system forms a potential field with minimal elastic energy at the grasp position.
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HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and
Shape Estimation
Jiefeng Li,
Chao Xu,
Zhicun Chen,
Siyuan Bian,
Lixin Yang,
Cewu Lu
CVPR, 2021
Project /
paper
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supp
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arXiv /
Code
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HandTailor: Towards High-Precision Monocular 3D Hand Recovery
Jun Lv, Wenqiang Xu, Lixin Yang, Sucheng Qian, Chongzhao Mao, Cewu Lu
BMVC, 2021
Paper /
arXiv /
Code
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BiHand: Recovering Hand Mesh with Multi-stage Bisected Hourglass Networks
Lixin Yang, Jiasen Li, Wenqiang Xu, Yiqun Diao, Cewu Lu
BMVC, 2020
Paper /
arXiv /
Code
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website template
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